Infinite-Dimensional Feature Interaction
Authors: Chenhui Xu, FUXUN YU, Maoliang Li, Zihao Zheng, Zirui Xu, Jinjun Xiong, Xiang Chen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments reveal that Infi Net achieves new state-of-the-art, owing to its capability to leverage infinite-dimensional interactions, significantly enhancing model performance. |
| Researcher Affiliation | Collaboration | Chenhui Xu1,2 Fuxun Yu1,3 Maoliang Li4 Zihao Zheng4 Zirui Xu1 Jinjun Xiong2, Xiang Chen1,4, 1George Mason University 2University at Buffalo 3Microsoft 4Peking University |
| Pseudocode | No | The paper provides mathematical formulations but no clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states that datasets are open-source but does not provide any link or explicit statement about the availability of the authors' own source code for their methodology. |
| Open Datasets | Yes | We conduct image classification experiments on Image Net-1K [9], which contains 1.28 million training samples belonging to 1000 classes and 50K samples for validation. We train the Infi Net-T/S/B/L models for 300 epochs with Adam W [28] optimizer. We evaluate our models for object detection tasks on widely used MS COCO [23] benchmark. We evaluate our models for the semantic segmentation task on widely used ADE20K [48] benchmark covering 150 semantic categories on 25K images, in which 20K are used for training. |
| Dataset Splits | Yes | Image Net-1K [9], which contains 1.28 million training samples belonging to 1000 classes and 50K samples for validation. |
| Hardware Specification | Yes | Image Net-1K experiments are conducted on 4 Nvidia A100 GPUs and Image Net-21K on 16 . |
| Software Dependencies | No | The paper mentions optimizers (Adam W) and schedulers (cosine learning rate) but does not provide specific version numbers for software libraries or frameworks (e.g., PyTorch version, CUDA version). |
| Experiment Setup | Yes | We train the Infi Net-T/S/B/L models for 300 epochs with Adam W [28] optimizer. We use the cosine learning rate scheduler [27] with 20 warmup epochs and the basic learning rate is set as 4 10 3. The training resolution is set as 224 224. More details can be found in Appendix A.1. (Tables 4 and 5 in Appendix A.1 provide extensive hyperparameter details including batch size, weight decay, label smoothing, and data augmentation settings.) |